Matrices are increasingly important in many computing tasks such as machine learning and other bulk data processing. Deep Learning is a class of machine learning algorithms. Deep learning architectures, such as deep neural networks, have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics and drug design.
Inference and training, two tools used for deep learning, are tending towards low precision arithmetic. Maximizing throughput of deep learning algorithms and computations may assist in meeting the needs of deep learning processors, for example, those performing deep learning in a data center.
General Matrix Multiply (GEMM) is a common algorithm in machine learning, and also in linear algebra, statistics, and many other domains. Convolution is also commonly applied in machine learning. Instructions for performing matrix compress and decompress operations are useful in performing convolution and GEMM algorithms in a machine learning context.